The Effectiveness of Cannabis and Cannabis Derivatives in Treating Lower Back Pain in the Aged Population: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/AIMS: Cannabis is increasingly used in the management of pain, though minimal research exists to support its use since approval. Reduction in stigma has led to a growing interest in pharmaceutical cannabinoids as a possible treatment for lower back pain (LBP). The objective of this review was to assess the role and efficacy of cannabis and its derivatives in the management of LBP and compile global data related to the role of cannabis in the management of LBP in an aging population. METHODS: A systematic review was conducted using predetermined keywords by 3 independent researchers. Predetermined inclusion and exclusion criteria were applied, and 23 articles were selected for further analysis. RESULTS: Studies identified both significant and insignificant impacts of cannabis on LBP. Contradicting evidence was noted on the role of cannabis in the management of anxiety and insomnia, 2 common comorbidities with LBP. The existing literature suggests that cannabis may be used in the management of LBP and comorbid symptoms. CONCLUSIONS: Further research is needed to consider cannabis as an independent management option. There is a lack of evidence pertaining to the benefits of cannabis in an aged population, and thus, additional research is warranted to support its use in the aged population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".